迈克尔·加菲尔德与戴维·克拉考尔谈进化、信息与《侏罗纪公园》

在某些情况下，实际上营养级甚至不再有定义。

And in some cases, in fact, the trophic levels even cease to be defined.

我们发现的关键是，如果我们想理解市场为何失灵，比如为什么波动性如此之高？

The key thing that we found is that if we want to understand why the market's malfunctioning, like why is volatility high?

为什么价格被错误定价？

Why are prices mispriced?

为什么它们偏离了基本面价值？

Why are they straying from fundamental values?

那么，生态中每种策略的财富水平决定了市场的错误定价程度。

Then the wealth of each of the strategies in the ecology determine how mispriced the market is.

而系统自身的自发动态可能导致显著偏离均衡状态，并引发严重的市场失灵。

And the system's own spontaneous dynamics can cause substantial excursions away from equilibrium and substantial market malfunctions.

是的。

Yeah.

这很有趣，因为我花了很多时间与加密货币圈的人交往。

This is it's funny because I've spent a lot of time consorting with people in the cryptocurrency community.

对吧？

Right?

在这个圈子里，人们非常关注的是降低交易成本。

And one of the things that people seem really keen on in that scene is in limiting the expense of transactions.

或者你看Robinhood，它如何让普通人也能参与股票市场。

Or like you look at Robinhood and the way that Robinhood opened up the ability for people to play in the stock market.

当我跟多因谈话时，你知道，多因对此非常担忧，对吧？

And when I talked with Doyne, you know, Doyne was very concerned about this, right?

因为这样一来，就类似于印刷术或互联网的演变，进入门槛降低到接近零，突然间每个人都能参与到这个系统中。

Because what happens then is something akin to what you see with the evolution of the printing press or the evolution of the internet, where the barrier to entry drops to close enough to zero that suddenly everyone can feed into a system.

而到了这个时候，原本维持秩序的垂直结构就会受到群体跟风行为的威胁。

And at that point, all of the vertical structures that were keeping things in order are imperiled by herd following behaviors.

所以你看GameStop这类事件，就会出现这种失控的结果，根本没有人真正掌控局面。

And so, you know, so you look at like GameStop and these kinds of things, and you get these runaway outcomes where no one is actually in charge.

于是就像一道闪电划过，所有人都开始狂奔。

And so you have a flash of lightning and everyone goes on a stampede.

但这只是其中一部分。

But that's one piece of it.

然后，在我和多因的对话中，另一个让我感兴趣的部分，实际上类似于我与凯文·凯利在《未来化石》中的对话，他谈到了‘渐进理想主义’。

And then the other piece of it that I liked in the conversation with Doyne is akin to a conversation actually I had with Kevin Kelly on Future Fossils, where he was arguing, he talks about protopianism.

对吧？

Right?

也就是说，我们永远无法达到乌托邦，但我们会做出这些渐进式的改进。

That like, we're never going to get to utopia, but we're going to make these incremental improvements.

他谈到了向城市化的人口结构转变。

And he talks about the demographic transition into urbanism.

他说，中国农村地区的人们纷纷迁往中国的巨型城市，他们的决策是基于自身经济机会的提升。

And he says, everyone moving from the rural areas of China into these mega cities in China is making decisions on the basis of their own increased financial opportunity.

这也回到了杰夫的研究，对吧？

So this gets back to Geoff's work as well, right?

当所有人都突然有能力抛弃丰富多彩的本地农业文化及其蕴含的智慧，任由这些文化消亡，转而参与全球经济时，问题就出现了——一切变得极度互联，也极度脆弱。

The problem with everyone being suddenly capable of abandoning the rich tapestry of local agrarian culture and the encoded wisdom in those cultures and allowing those things to decay as they move into the ability to participate in a global economy is suddenly everything becomes hyper connected and very, very brittle.

我们在新冠疫情中就看到了这一点。

And we saw this with COVID-nineteen.

实际上，我得找一下，但最近我读到一篇非常有趣的博客文章，名为《欢迎来到侏罗纪公园》，将约翰·哈蒙德将侏罗纪公园所有流程自动化的决定，与准时制供应链引发的连锁故障进行了比较。

Actually, I'll have to dig it up, but I read a really interesting blog post recently called Welcome to Jurassic Park comparing John Hammond's decision to automate all of the processes of Jurassic Park with the way that just in time supply chains caused cascading failures.

当我邀请马修·杰克逊做客节目时，他谈到高度互联的银行网络，我们看到这种现象：将一切连接起来所带来的便利，同时也制造了脆弱性。

When I had Matthew Jackson on the show and he was talking about hyper connected bank networks, we see these things where the convenience afforded us by patching everything together creates a vulnerability.

所以，现在有一种时代精神正在兴起，我认为即使在复杂性科学领域之外，人们也因为新冠疫情、银行倒闭以及种种事件，开始意识到为什么——正如我们在节目中反复提到的——需要保持孤立的区域。

So like there's something going on in the zeitgeist right now where I think even outside of the field of complexity science, people are starting to, because of COVID, because of bank failures, because of all of this stuff, people are starting to understand that there are reasons why, and this is something we've brought up time and time again on the show, there are reasons why you want isolated pockets.

你需要有储备，你知道，要让某些东西脱离网络。

You want reservoirs, you know, you wanna keep stuff off of the network.

你需要在系统中留出空气间隙。

You want air gaps in things.

因此，你也需要某种垂直性。

And consequently, you also need some kind of verticality.

最终，这其实是在为象牙塔、为新闻业的标准这类事物辩护。

Like there's ultimately an argument for the ivory tower and for there being standards to journalism and this kind of thing.

这确实很有趣。

Well, that's it's interesting.

好的。

Okay.

这让我们稍微转向下一集，因为我们已经讨论过生态和不稳定性。

So this takes us a little bit to the next episode because we've talked about ecology and instabilities.

生态学主要关注能量和物质的交换。

Ecology focuses largely on energy, exchanges of matter.

但当然，你在两个例子中提到的另一个维度——比如区块链和GameStop——实际上是关于计算和信息共享的。

But of course, the other dimension that you talked about in the two examples you gave, you know, in blockchain, GameStop, it's really about computation, the sharing of information.

我们社区中的许多人，尤其是梅兰妮·摩西和黛博拉·戈登，已经指出，在自然界中存在高度连接的类似蜂群的系统，即真社会性昆虫：蜜蜂、蚂蚁和黄蜂，它们 somehow 解决了这个难题。

And many people in our community have put it out, most notably, Melanie Moses and Deborah Gordon, that there are highly connected Borg like systems in the natural world, namely the eusocial insects, bees, ants, and wasps, that somehow have squared the circle.

它们具有这种特性，却依然保持稳定。

They have that kind of characteristic and yet remain stable.

我想我们刚刚听到了黛博拉·戈登关于社会性昆虫计算的这段摘录。

And I think we just listened to this excerpt from Deborah Gordon on social insect computation.

因此，我认为有必要从蚂蚁研究的历史谈起，探讨我们所观察到的蚂蚁行为多样性，以及我们在此过程中引入的那些有问题或不准确的类比和隐喻性语言。

So it makes sense to anchor this, I think, in a bit of the history of research on ants and on the diversity of behaviors that we see ants engaged in and the problematic or inaccurate analogies and, like, metaphorical language that we're bringing into this.

你提到了亚当·斯密创造了‘劳动分工’这个术语，这似乎引发了一系列在昆虫学界对蚂蚁研究中的假设，而你在这段论述中非常清晰地批判了这些假设。

You know, you mentioned Adam Smith here coining this term, the division of labor, and that seems to set off this whole cascade of assumptions in entomology when people are looking at ants that you critique very articulately in this, I think.

是的。

Right.

因此，你认为，尽管蚂蚁长期以来被研究人员和公众普遍理解为如此，但‘劳动分工’这个概念其实并不适用于蚂蚁。

So this idea, you argue, is not appropriate for ants, even though it certainly seems to be the way that ants are commonly understood and have been understood by researchers for a while.

你能谈谈关于这一问题的研究历史吗？你是如何挑战这种误解的？

Could you talk a little bit about the history of the research on this and how you attack this particular misunderstanding?

当我20世纪80年代刚开始研究蚂蚁、还是研究生的时候，主流观点认为每只蚂蚁的任务或功能都是由基因决定的。

When I started doing research on ants in the 80s, when I was a graduate student, the prevailing idea was that each ant had a task or a function that was genetically determined.

因此，如果你想理解不同蚁群之间的差异，或者行为是如何演化的，你就会去观察每种任务中蚂蚁的分布情况。

So that if you wanted to understand how colonies are different or how behavior evolves, you would look at the distribution of ants in each task.

在这种观点下，一个工蚁数量更多的蚁群，就会进行更多的觅食活动。

So in this view, a colony that has more foragers would do more foraging.

因此，如果多进行觅食是好事，那么进化就会青睐那些拥有更多觅食蚁的群体。

And so if it was a good thing to do more foraging, then evolution would favor colonies that had more foragers.

这种思维方式将蚂蚁行为的所有原因都归结于蚂蚁个体内部。

So that way of thinking locates all the causes of the ants' behavior inside the ant.

这种思维方式其实也适用于我们理解大脑的工作方式。

And it's really the same way of thinking that, we could use to understand how a brain works.

我们可以说，每个神经元都有特定的职责，只要我们能列出每个神经元的所有任务，就能理解大脑是如何运作的。

We could say that each neuron has a certain job, and if we could only list all the tasks of every neuron, we would understand how the brain works.

或者你也可以这样说：生物体内的每个细胞都有特定类型，而生物体的整体行为就是这些不同个体组件执行各自任务的总和。

Or you could say the same thing about cells in an organism, that each cell is certain type, and then what the organism does is the aggregate of all those different individual components carrying out their tasks.

但显然，自然界并不是这样运作的，因为我们看到，当周围环境发生变化时，个体部分的功能也会随之改变。

But it seems pretty clear that nature doesn't work that way because we see that individual parts change function when what's going on around them changes.

关于蚂蚁，我所了解到的是，单个蚂蚁会切换任务。

And with respect to ants, what I learned is that individual ants switch tasks.

因此，同一只蚂蚁并不会总是做同样的事情。

And so the same ant doesn't always do the same task.

而且，即使你认为一只蚂蚁今天被分配了某种任务——比如今天它是一只觅食蚁——这仍然无法告诉你这只蚂蚁会进行多少觅食活动，或者它什么时候会出去觅食。

And there's another side to it that even if you consider an ant to be assigned a certain task today, today this ant is a forager, that still doesn't tell you how much foraging that ant is going to do or when that ant is going to go out and forage.

这意味着，你不能仅仅通过统计每种类型的蚂蚁数量来理解整个蚁群的行为，因为还有其他过程——来自蚂蚁之间的互动以及蚂蚁与周围环境的互动——决定了哪只蚂蚁执行什么任务，以及它是否现在就执行。

So that means you can't really understand what the colony is doing by listing the numbers of ants of each type because there's other processes that come from interactions among ants and interactions with the world around the ants that determine what ant does which task and whether it does it right now.

是的。

Yeah.

因此，关于蚂蚁任务分配具有流动性的这一观念，随着时间的推移逐渐发展起来。

So this notion of this fluid task allocation of ants has developed somewhat over time.

我的意思是，你提到在八十年代，人们已经意识到蚂蚁的行为并不仅仅受限于其身体类型，比如体型或特定部位的大小。

I mean, you mentioned that there was a time in the eighties when there was some understanding that ants were not merely limited by their body type, by size or size of specific parts.

你知道，普通人通常会想到工蚁和兵蚁的区别。

You know, people think about workers versus soldiers, I think, in the lay understanding.

但后来，至少在一段时间内，这种观点被‘多态性’所取代，即蚂蚁会随着年龄增长而改变功能。

But that was replaced, at least temporarily, by this polyethism, this notion that ants are changing function as they age.

我的意思是，你提到这种现象确实在发生，但仅凭这一点还不足以充分描述这里所发生的情况。

And that's not I mean, you mentioned that that's going on, but that's not adequate to describe what's going on here.

对吧？

Right?

那么，你和你的同事们发现了什么，证明这种解释是不够的？

So what was it that you and your colleagues found that demonstrated that that was not a sufficient explanation?

好吧，我们先回到这一点。

Well, let's go back to that for a second.

蚂蚁和蜜蜂的共同点是它们都生活在群体中。

So ants and bees have in common that they live in colonies.

它们协同工作。

They work collectively.

其中有一只或多只繁殖雌性，我们称之为蜂后，尽管它们并不指挥其他个体，只是产卵。

There is one or more reproductive females that we call queens, although they don't tell anybody what to do, and they lay the eggs.

而你所看到的四处飞行或爬行的蚂蚁或蜜蜂，都是不育的雌性工蚁或工蜂。

And then all the ants or bees that you see flying around or walking around are sterile female workers.

如今，人类已经驯化蜜蜂一万年了，它们被选择来改变任务。

Now honeybees have been domesticated by people for ten thousand years, and they have been selected to change tasks.

所以我们希望蜜蜂出去觅食，采集花粉，携带花粉并为我们的作物授粉。

So what we want bees to do is to go out and forage and collect pollen and carry the pollen around and pollinate our crops.

因此，我们选育了蜜蜂，使它们在年轻时在巢内工作，年长时外出觅食。

And so we have selected bees to make the transition from working inside the nest when they're younger to going outside and foraging when they're older.

众所周知，蜜蜂会改变工作内容。

So that's very well known that bees change tasks.

这一点在蚂蚁中被稍微混淆了。

That got a little bit confused with the idea in ants.

实际上，只有一小部分蚂蚁物种，不是所有蚂蚁物种，它们的工蚁有不同的体型。

Actually, minority of species, just not all ant species, but some ant species, the workers come in different sizes.

它们都是成年个体。

They are adults.

它们不会从一种体型长成另一种体型。

They don't grow from one size to another.

所以一只蚂蚁从蛹中羽化出来时，要么是小的，要么是大的。

So an ant, when it emerges from the pupa, is either a small one or a larger one.

人们认为这些体型差异与任务相关，即在那些具有不同体型蚂蚁的物种中，一种体型的蚂蚁负责一种任务，另一种体型的蚂蚁则负责另一种任务。

And the idea was that those sizes were associated with tasks so that in those species where there were ants of different sizes, the the ants of one size would do one thing and the ants of another size would do another thing.

因此，关于这一机制实际上存在两种截然不同、彼此难以调和的观点。

So there were really two different views of how it works that in fact don't fit together very well.

一种观点来自蜜蜂，认为蜜蜂会从巢内工作逐渐转为外出工作，即在其一生中会改变任务。

One was the idea from honeybees that a bee moves from working inside to outside so she changes tasks over her lifetime.

另一种观点则认为，不同体型的蚂蚁各自被分配固定任务，并且只执行该任务。

And the other was the idea that ants of different sizes are each assigned a task and they just do that task.

于是，人们开始研究蚂蚁中的时间多态性，发现蚂蚁和蜜蜂一样，也会从一种任务转向另一种任务。

So people started to look at this idea of temporal polyethism in ants and see that ants do, like bees, move from one task to another.

更进一步的是，要理解这种转变如何响应环境变化而发生。

The further step is to understand how that changes in response to changing conditions.

因此，蚂蚁并非仅仅沿着某种预设的轨迹从一种任务转向另一种任务，而是整个群体根据环境变化，动态调整分配到不同任务的蚂蚁数量。

So it isn't just that an ant moves from one task to another along some predetermined trajectory, but instead the colony shifts around the numbers of ants allocated to different tasks as conditions are changing.

例如，如果食物增多，蜂群可能会分配更多蚂蚁外出采集食物。

So for example, if there's extra food, a colony might allocate more ants to go out and get the food.

在红火蚁中，这是一种我研究很多的蚂蚁物种，如果食物增多，其他任务的蚂蚁就会转去做觅食者。

In harvester ants, which is a species of ant that I've worked on a lot, if there's more food, then ants from other tasks will switch to become foragers.

它们并不是因为体内发生某种变化而被触发去成为觅食者。

They're not triggered to become foragers by something that happens inside them.

它们的触发机制是食物的可得性，以及蚁群通过互动集体动员更多蚂蚁去觅食的过程。

They're triggered by the availability of food and the process that the colony has collectively for using interactions to get more ants to forage.

这回答了你的问题吗？

Does that answer your question?

是的。

Yes.

所以，我喜欢这一点的是，黛博拉·戈登打破了蚂蚁群体中存在生物决定的角色和分工这种神话。

So, yeah, the thing that I liked about this was Deborah Gordon pops the balloon of the myth that what you have is a kind of like a biologically determined order of roles and casts in an ant colony.

这几乎像是对进化动态的一种优生学式误读。

That these, it's almost like a eugenic misinterpretation of evolutionary dynamics.

她说，实际上，真正发生的是，通过减少这种固定分工——等等，我们之前在节目中提到过另一篇论文。

And she says, no, actually what's going on is that by diminishing, actually there was, you know, there was another paper that we brought up on the show a lot.

我认为阿尔伯特·刘参与了这项研究，探讨了通过减少网络中个体节点的记忆，如何能让适应性转变更容易传播。

I think Albert Kao was involved in this research on the way that by reducing the memory of individual nodes in a network, then you can allow adaptive transformations to propagate more easily.

而长记忆系统则显得相当固执。

Whereas like long memory systems are kind of stubborn.

因此，里卡多·索勒所说的‘液态大脑’的美妙之处在于，蚂蚁足够愚钝，能够作为群体智能运作。

And so the beauty of the liquid brain in Ricard Soleil's terms of an ant colony is that the ants are dumb enough that they can function as a hive mind.

这正是博格人与复杂性科学实际观点之间的关键差异：你必须削弱个体的能动性，因为，在过去四年半里我读过的一篇最喜爱的论文就是杰西卡·弗拉克关于‘粗粒化’作为下行因果作用的研究，对吧？

And so this is the thing about the Borg that has always kind of stood out in contrast to what complexity science is actually saying, which is that you have to diminish the individual agency because, you know, like one of my favorite papers that I read in the last four and a half years here was Jessica Flack's work on coarse draining as downward causation, right?

当这些新系统出现时，它们会施加一种影响，这几乎就像西蒙·德迪奥所讨论的‘聚合体’，对吧？

The And way that as these new systems emerge, they exert a kind of, it's almost like an, like Simon DeDeo talks about this also, aggregors, right?

这些在事物交汇处涌现的新存在，就像干涉图案一样。

Where these like these new beings that emerge at the intersections of things like an interference pattern.

然后你有了社会契约，我们所有人实际上都在以某种方式与社会契约互动，而不是彼此直接互动。

And then you have a social contract and all of us are interacting in some way with the social contract more than we are actually interacting with each other.

音乐家在乐队动态中也会谈到这一点，或者我会从作为这个组织主席的责任角度来思考：总有一个更高的层面在塑造着我们作为个体互动的方式。

Musicians talk about this when they're in band dynamics, or I think about this in terms of, you know, your responsibility as the president of this organization, where it's like, there is always that supervening layer that structures the way that we can interact as individuals.

我认为，大多数人对技术以及人工智能最近的发展感到恐惧，是因为他们意识到，这些技术正越来越多地决定着我们作为人类所能选择的行动方案。

And I think what most people are afraid of with technology and the developments that AI has been taking recently is that they realize that this is increasingly determining the plays that are available to us as people.

这挑战了现代人自我塑造的神话。

And it's challenging the sort of myth of the modern self authoring self.

是的。

Yeah.

你提出的这个观点非常有趣，也是我们许多人一直担忧的问题，这实际上马上引出了下一个话题，正如你所指出的，我们所知的最复杂、最具连接性的系统是中枢神经系统，也许是哺乳动物的中枢神经系统。

It's a very interesting observation that you make, and it's something that many of us have been worried about, which is And it actually takes us to the next episode in a second, which is exactly as you point out, that the most sophisticated, if you like, highly connected system that we know is the central nervous system, maybe the central nervous system of mammals.

我并不是说单个神经元像我们过去描绘的那样简单。

And the individual neurons, I'm not saying they're as simple as we have portrayed them as.

它们不仅仅是整合与触发的二元单元。

They're not just integrate and fire binary units.

尽管如此，它们并不那么复杂，并且在神经系统中，它们被其制度性角色所束缚。

Nevertheless, they're not that sophisticated, and they are enslaved, if you like, by their institutional commitments in the nervous system.

我们从未真正经历过一种单位比整体更复杂、或者甚至相当复杂的局面。

We haven't really experienced a world where the units are as sophisticated or, quite frankly, more sophisticated than the aggregate.

我们正在构建一种新型的复杂系统。

And that that is a new kind of complex system that we're building.

这让我稍微转向下一集，也就是泰勒的那集。

And it does take me a little bit to the next episode, and that's Tyler's episode.

我有个问题想问你，因为在那一集中，泰勒谈到了爵士乐队中的人类创造力，但他用的是统计物理学的模型，这些模型描述的并不是具体的音乐家。

And this is a question that I have for you because in that episode, Tyler talks about human creativity, for example, in jazz ensembles, but using models from statistical physics, which aren't individual musicians.

它们实际上是具有上下自旋的铁磁体。

They're actually ferromagnets that have up and down spin.

在我们的工作中，我们一直面临这种张力：现成的模型是为那些并非我们真正关心的系统开发的，而你一开始提到的正是我们关心的系统——那些具有能动性的系统，其中的单元不仅仅是上下自旋。

So we've always been we have this tension right in our work, which is the models that are available, if you like, off the shelf were developed for systems which aren't really the ones that we care about, which is how you started, which is systems that have agency, where the units aren't just up and down spins.

它们是迈克尔·加菲尔德。

They're they're Michael Garfield.

他想去跳舞、创作，或者制作一集播客。

He wants to go and dance or or compose or or or make a a podcast episode.

让我们先听一小段泰勒的发言。

So let's just listen a little bit to Tyler.

我想听听你对这一点的反思。

I'd like to hear your reflections on that.

我们所认为的即兴演奏，可能实际上和我们所看到的作曲过程是同一回事，只是发生在不同的时间尺度或空间尺度上。

What we regard as improvisation might be kind of the same thing that we're seeing going on with composition, but at a different time scale or or spatial scale.

但这都是对这篇论文的进一步延伸，我们有责任先真正讨论这一点，而不是直接跳入那个话题。

But that's all sort of a meta on this paper, and we we we have a responsibility to actually talk about this before we leap into that.

因此，这里最好的起点似乎是参考像圣塔菲研究所外部教授马丁·谢弗这样的人所提出的先例，他们探讨了在这些临界点或过渡时刻究竟发生了什么，以及我们如何识别可以用来预测这些时刻的特征。

So it seems like the place to start here would be in the precedent set by people like SFI external professor Martin Sheffer talking about what is actually going on at these thresholds or these these transitional moments and how it is that we can identify the features that we can look for to anticipate them.

那么，先梳理一下其中的一些核心概念，然后你和你的合作者是如何试图识别你们所研究的音乐中那些能够量化这一切的特征的呢？

So laying out some of the core concepts there and then how you and your coauthors sought to identify the features in the music that you were examining that would allow you to quantify all this?

完全正确。

Totally.

是的。

Yeah.

所以马丁·谢弗是一位生态学家。

So Martin Scharf is an ecologist.

他和许多其他生态学家一直在努力识别生态系统即将发生关键性转变的通用早期预警信号。

And he, along with a number of different ecologists, have been trying to identify generic early warning signals that an ecosystem is about to undergo some kind of critical transition.

对吧？

Right?

你可以想象一个湖泊，从一个健康、繁荣、水质清澈的状态，突然转变为所有鱼类死亡、藻类暴发的灾难性状态。

So you can imagine a lake that goes from a really healthy, thriving, clear watered state to one where all the fish die off and you get a sudden catastrophic algal bloom.

我们能否提前知道这种情况即将发生？

Can we know that that's about to happen?

这些极具智慧的生态学家们借鉴了一些最初源于统计力学（广义上属于物理学）的技术工具，用于预测相变的发生。

And what those really clever ecologists have done is taken some technical tools that were originally introduced in statistical mechanics, sort of physics broadly, looking at when we can predict phase transitions.

其核心理念是，当一个系统处于这些转变的边缘时，它在某种程度上已经丧失了恢复力。

And the idea is that when a system is perched on the edge of one of these transitions, it's lost resilience in some way.

检验这一点的一种方法是对系统进行扰动。

And one way that you can test for that is you can poke the system.

以湖泊的例子来说明。

So imagine the lake example.

你进去后，可能杀死一大批鱼，或者大量增加鱼的数量。

You go in and maybe you kill off a bunch of the fish or you add a whole bunch more.

然后观察系统能够多快地恢复到原本健康、正常运作的状态。

And you see how rapidly the system is able to bounce back to return to its, you know, healthy, happy functioning state.

如果你测量一下这种恢复时间，也就是系统被扰动后恢复所需的时间，就能很好地判断系统的恢复力有多强。

And if you sort of measure that return time, the time it takes for the system to bounce back after you poke it, that gives you a good sense of how resilient the system is.

你希望它能迅速恢复，比如：‘你扰动了我，但我已经回来了，一切如常。’

You want it to sort of really rapidly be like, okay, you poked me, but I'm back I'm back to usual.

然而，在许多我们想要研究的系统中，我们实际上无法进去进行扰动。

Now, in a lot of systems that we want to study, we don't have the ability to go in practically and poke them.

对许多大型健康生态系统进行扰动是不负责任的行为。

It would be irresponsible to poke a lot of like big healthy functioning ecosystems.

因此，你可以让系统自行运转，就像它自然生活一样，然后观察系统的噪声结构。

And so what you can do instead is you just let the system sort of work on its own, so sort of like living out its life, and you look at the noise structure of the system.

当系统因内部自然噪声而上下波动时，事实上有一些可以计算的重复性指标，能告诉你系统的恢复力如何。

As it bounces up and down, just on the basis of natural noise in the system, there are, it turns out, some recurring measures that you can calculate that tell you how resilient system is.

因此，你可以观察自相关性、方差或波动。

So you can look at autocorrelation, or variance, or flickering.

这些是某些技术性术语，指的是你可以进行的不同计算，用以了解系统多快会遗忘这些干扰、恢复到其自然的稳定状态。

These are sort of technical terms for different calculations you can do that can give you an idea of how quickly the system forgets these pokes, these prods, and returns to its natural resting state.

我的想法是，这些韧性指标在爵士即兴演奏这个‘生态系统’中也可能同样有效。

Now my idea was that these measures of resilience might work just as well in the quote ecosystem of jazz improvisation.

没错，实际上这是在运用生态系统这一隐喻，尽管这仅仅是一种表达方式。

Right, so really drawing on this ecosystem metaphor when really that's sort of a way of speaking.

我想表达的是，在爵士即兴演奏中，多个元素以分布式的方式相互作用。

What I want to say there is that in jazz improvisation you have multiple elements that are interacting with each other in a distributed way.

我本可以称其为生态系统、经济体系，或者干脆直接称之为复杂系统。

And I could have called that an ecosystem or an economy or you know just flat out called it a complex system.

但关键是，在这类由分布式元素以非线性方式相互作用的系统中，有时你能够提前预见到即将导致突发性灾难性转变的韧性丧失迹象。

But the idea is that in these kinds of systems where you have distributed elements interacting in non linear ways, you can sometimes foresee this loss of resilience that precedes a sudden catastrophic critical transition.

因此，我们着手尝试使用马丁·谢弗等学者为自然生态系统所开发的那些工具。

And so we set out to try to use the tools that had been deployed so well by folks like Martin Sheffer and others for natural ecosystems

人们不喜欢它，恰恰是因为你提到的那些原因。

People don't like it for precisely the reasons that you mentioned.

然而，回到涌现的认识论这一整个问题，以及我希望与杰西卡·弗拉克进行的对话，当她发表关于沙漏涌现模型的论文时。

And yet going back to the whole question of the epistemology of emergence and going back to the conversation I hope to have with Jessica Flack when she gets her papers on the hourglass emergence model published.

我非常喜欢，我的意思是，杰西卡的方法如此具有挑衅性，正是因为它颠覆了我们一开始讨论的观点，即复杂性是从简单性中产生的。

I love, I mean, Jessica's approach is so provocative precisely because it undermines what we were talking about at the beginning, where this idea that what's going on is that complexity is arising from simplicity.

它暗示这些实际上是横向的问题。

And it suggests that actually these are horizontal issues.

我并不是说蚂蚁会坐在那里想，‘我真希望现在能看奈飞’之类的。

And I'm not saying that ants are sitting there saying, Oh, I wish I were watching Netflix right now, or whatever.

但确实存在一个维度，比如当海法大学的阿迪·利夫纳特在2018年的发育偏差研讨会上做报告时，他说，基因调控网络中的基因融合似乎遵循与大脑中赫布式‘一起放电、一起连接’学习相同的原理。

But there is a dimension in which, like when Adi Livnaut of Haifa University came and presented at the Developmental Bias Workshop in 2018, and he said, the fusion of genes in gene regulatory networks seems to be governed by the same principles as Hebbian fire to wire learning in the brain.

因此，许多突变并不像我们以往认为的那样是随机的，而是实际上遵循着热力学梯度，对吧？

And therefore a lot of mutation is not random in the way that we have understood it to be, but is actually following a thermodynamic gradient, right?

因此，很多人已经讨论过这一点：比如，在个体层面，你可以说，数百万人开车进入纽约，他们各自决定去上班；但当你退得足够远，从统计学角度看，我们知道每天会有五百万人开车经过那座桥。

And so a lot of people have written about this where it's like, yeah, at the level of the individual, you can say, millions of people are driving into New York, they're making the decision to go to work, but then you back out far enough and you can say, statistically, we know five million people are gonna be driving over that bridge every day.

这并没有真正触及到我喜爱泰勒这一集的另一点，即创作过程中某种关键时刻的存在。

And this doesn't really get to the other thing I love about Tyler's episode, which is this relationship between the way that there's a kind of moment in the creative process.

然后，正如我们之前稍微讨论过的，这种自相关性有时看起来就像一次大规模灭绝事件。

And then the way that, as we were talking about a little bit earlier, the way that this kind of auto correlation also looks like a mass extinction event sometimes.

这两点是这里的关键。

Those are two key pieces here.

让我举个好例子，这其实也是一个过渡——新冠疫情，因为我认为我们在疫情后观察到的许多社会政治极化现象，都关乎个人能动性与制度控制。

So let me, but actually a good example, and again, it's a segue was COVID because I think a lot of the social political polarization that we observed following an epidemic was about individual agency and institutional control.

口罩佩戴在某些群体看来是一种不可接受的强制或对个人自由的限制，而在另一些人眼中则是为了更服从集体利益所必需的。

Mask wearing was perceived as, to some communities, an unacceptable imposition or restriction of individual freedom and others as necessary so as to be more beholden to the collective good.

它完全具备你所描述的这些特质。

It had exactly these qualities you're talking about.

实际上，西蒙在那一集中也反思了个人能动性与个人理性或非理性之间的关系。

And Simon, actually, in that episode does reflect on issues of individual agency in relation to individual rationality or irrationality.

因此，值得听一听西蒙访谈的这一部分。

So it's worth listening to this section, of an interview with Simon.

在你讨论天体物理学作为一门成功的科学，然后逐渐转向研究失败情景的过程中。

In your discussion of astrophysics as a successful science and then your drift away from that into the, you know, the study of failure scenarios.

迈克尔，我并不直接这么说，但我或许可以打个比方。

I I don't say, Michael, I may I may use a metaphor here.

这不是骨折。

It's not broken bones.

这是一种自身免疫性疾病。

It's an autoimmune disease.

对吧？

Right?

这是一种情况，身体的美德变成了创伤，变成了恶习。

It's a case where the virtue of the body turns into a trauma, turns into a vice.

我们的免疫系统，就像这些庞大的系统之一，原来我们体内有一个自主的分子无人机系统，时刻为我们作战，这很好，但一旦不好，事情就会急剧、显著且长期地出错。

Our immune system, again, like one of these, you know, mega things, like it turns out we have this autonomous molecular drone system constantly attacking on our behalf, and that's great until it's not great and, you know, things go drastically, dramatically, and chronically wrong.

所以你可能会说，我现在研究的是认识论的自身免疫疾病——那些让我们成为强大科学家的特质，同样也可能让我们成为强大的阴谋论者，这正是我们目前感兴趣的问题之一。

So you might say like, I studied the autoimmune diseases of epistemology now, how the same things that make us such powerful scientists can also make us such powerful, let's say, conspiracy theorists is the thing one of the things we're interested in right now.

但是我打断你了，所以请继续，布莱恩。

But I I interrupted you, so please please go on, Brian.

嗯，这很棒，因为你正好给了我一个绝佳的机会。我想和你讨论一篇你与扎卡里·韦斯特肖维奇合著的论文，《从概率到一致性：解释性价值如何实现贝叶斯推理》。

Well, this is great because you just handed it to me on a platter Cause I wanted to talk with you about a paper that you coauthored with Zachary West Showich, From Probability to Consilience, How Explanatory Values Implement Bayesian Reasoning.

现在我猜想，大概有一半的

And now I imagine probably half of

听众知道贝叶斯概率是什么，而另一半则不知道。

our audience knows what Bayesian probability even is and the other So half does

就我们的目的而言，你可以随意深入探讨这个话题，但嗯。

for our purposes, you know, feel free to dip into that as much as you'd like, but Mhmm.

我们真正想了解的是你提到对自身免疫性疾病感兴趣。

What we're really after is you talk about being interested in the autoimmune disease.

我认为这篇论文很好地剖析了人们如何试图理解事物，以及什么构成了健康的免疫系统，也就是一种平衡的理解启发式方法。

And I think this paper does a very good job of unpacking how it is that people try to make sense of things and what sort of constitutes a healthy immune system, meaning, you know, like a balance of different heuristics for understanding.

那么请先带我们了解一下这篇论文，然后我们再以此为基础展开讨论其他内容。

So lead us into this piece a little bit, and then we'll start tethering out from it into other stuff.

不。

No.

这其实是个绝佳的起点，因为那篇论文是一篇理论性文章。

This I mean, it's a wonderful place to begin because that paper it's a paper in theory.

它探讨的是知识构建的理论，以及知识构建究竟是如何发生的。

It's a paper on the theory of both knowledge building and how knowledge building actually happens.

进入这个话题的一个途径就是贝叶斯推理。

One way into this is Bayesian reasoning.

那么，什么是贝叶斯推理？

So what is Bayesian reasoning?

它与贝叶斯牧师有关，他死后才发表了一篇论文，而且他本人甚至都没有提交它。

It's associated with the reverend Bayes, publishes one paper posthumously, and he didn't even submit it.

有人发现后说：嘿。

Somebody else was like, hey.

我找到了这些笔记。

I got these notes.

这位是贝叶斯牧师。

This is the reverend Bays.

这篇论文发表于十八世纪的《皇家学会哲学汇刊》，今天你读它时，会忍不住说：天哪。

Submitted to the philosophical transactions of the royal society in the seventeen hundreds, and you read it today, and you're like, holy shit.

这家伙竟然预见了三百年后的未来。

Like, this guy saw three hundred years into the future.

他准确预测的内容多得令人难以置信。

It's insane how much he gets right.

而你正在阅读它。

And you're reading it.

我的意思是，你会一直读下去。

I mean, it's like you keep going.

我读过的唯一另一篇类似这样的论文是克劳德·香农创立信息论的那篇，同样也只靠一篇论文就完成了。

It's actually the only other paper that's like this that I've ever read is Claude Shannon's creation of information theory, which again happens in one paper.

贝叶斯牧师的这篇论文也是如此。

Reverend Bays' paper, it's the same thing.

这只是稍微有点老式的语言风格，但你读着读着就会觉得，这肯定是论文的结尾了。

It's just slightly old style language, but you're reading it and you're like, I'm sure this is the end of the paper.

但其实并不是。

And it's like, no.

然而，他理解的东西还要更多。

And yet, there's even more that he understands.

有趣的是，我们直到二十世纪才真正意识到其中包含了多少内容。

And then what's funny, of course, is that we didn't actually realize how much was in there until the twentieth century.

所以当时，我的意思是，人们是理解的。

So at the time, I mean, people understood it.

人们？谁知道他们是否真的理解了呢？

People well, who knows if they understood it?

但你知道，它发表在了这个国家乃至整个欧洲最顶尖的期刊上。

But, you know, it was published in the best journal in the country, if not all of Europe.

但与进化论、电磁学的发现，或者本·富兰克林放风筝之类的事不同，我们却没有为此设立纪念碑，我想这是因为，直到我们开始努力构建可靠的知识体系时，才意识到这个问题有多么重要。

But unlike, let's say, the theory of evolution, unlike the discovery of electromagnetism, unlike, you know, Ben Franklin flying his kite, we don't have monuments to bathe in part because I think we didn't realize how important the question was until we started trying to form reliable knowledge.

所谓可靠的知识，指的是数学上可靠的知識，当我们的测量设备足够先进，能够获得真正的定量证据而非仅仅是定性证据时，这种知识就变得至关重要。

And by reliable knowledge, mean mathematically reliable knowledge, and that became a premium when our measurement devices got good enough that actually we were really getting quantitative evidences, not just qualitative evidence.

然后，当然，我们还具备了通过机器收集大量信息的能力。

And then, of course, we also had the ability to gather a great deal of information through machines.

最后，当然，也许我们拥有了足够的计算能力来实现这一点，因为贝叶斯曾说：‘看，这就是你最优地推断事物的方法。’

And finally, of course, maybe we had the processing power to do it because Bayes was like, oh, look.

这就是你如何最优地推断事物的方法。

Here's how you would infer things optimally.

祝你好运吧。

Good luck with that.

对吧？

Right?

我得走了。

Gotta go.

而我们当时真的没有那种大规模开展这项工作的工具。

And we really didn't have, you know, the tools to do this on a mega scale.

直到二十世纪，它才真正影响到科学。

It didn't really hit science until the twentieth century.

虽然不一定要把所有事情联系起来，但天文学中发生的一件事就是贝叶斯推理进入了天文学，正是从那时起，我们才真正进入了所谓的精确时代——我可以准确地告诉你，宇宙的年龄是127亿年，误差大约在十亿年左右。

Not to tie everything together, but this is of course one of the things that happened in astronomy was Bayesian reasoning hit astronomy and that's when we finally entered what we call today the precision era where I can literally tell you the universe is 12,700,000,000 years old plus or minus you know a billion or whatever it is.

所以，绕了这么一大圈，我想说的是，贝叶斯推理讲述的是如何最优地形成信念。

So that's a long way around saying Bayesian reasoning is a story about how to form beliefs optimally.

你可以证明，使用这种方法能比任何其他方法更快地获得正确且无偏的信念。

You can prove that you will achieve the correct unbiased belief faster than any other method.

这很棒。

So that's great.

意思是，现在我们可以去听音乐了，对吧？

Like, now we can just go play music, right?

我们完成了。

We're done.

我们已经解决了知识形成的问题。

We've solved knowledge formation.

我们有一个明确的配方，一个算法，真正地用于形成最可能的信念。

We have a recipe, an algorithm, literally, for forming the best possible beliefs.

事实上，现在甚至出现了对贝叶斯牧师的崇拜，有些人真的认为这个问题已经被解决了。

And in fact, like, it turns out there are, like, cults of the Reverend Bays at this point where people actually believe this problem has been solved.

当然，问题并没有被解决，但之所以没解决的原因非常有趣，而原因又回到了所谓的先验这一概念——至少我们现在这么称呼它。

Of course it hasn't but the reason it hasn't been solved is very interesting and the reason comes back to this question of what are called priors, at least that's what we've come to call them.

事实上，贝叶斯推理让你能够从某种先前的状态中提升你的知识。

So it turns out actually what Bayesian reasoning enables you to do is to increase your knowledge from some previous state.

它能让你从A点的知识状态出发，通过收集信息，推进到B点，因此你可以把它看作是从A到B的最优方式。

It enables you to take your state of knowledge at point A and increase it by gathering information to take you to point B and so it's actually the might think of it as the optimal way to go from A to B.

但它并没有告诉你，你是如何到达A点的。

It doesn't however tell you how you got to A.

它没有告诉你你的起点在哪里。

It doesn't tell you where you begin.

它也没有告诉你，当你走进实验室、进入一个新领域，比如圣塔菲研究所，而你之前从未在科学研究中见过任何生物时，该如何在证据出现之前区分不同的解释。

It doesn't tell you when you walk in to a laboratory, when you come into a new field, you know you come into the Santa Fe Institute and you've never seen an animal before, at least in a scientific study, it doesn't tell you how you ought to, for example, distinguish between explanations before the evidence comes in.

它并没有告诉你，在面对不同证据时，应该如何更多或更少地关注某一部分。

It doesn't tell you the ways in which you might attend more or less to some chunk of evidence over another.

因此，这里面有很多缺失的环节，我和扎克正试图将它们一一厘清，我想说，这里的创新部分在于，作为心理学家和认知科学家，我们把注意力引向了‘解释’本身这个议题。

So there's a lot of these missing pieces in there that, you know, Zach and I are trying to tease apart, and so I would say the innovation there is in part obviously drawing our attention as psychologists, as cognitive scientists, to the problem of explanation itself.

所以，这非常元层级——我们正在解释‘解释’。

So, you know, it's very meta, we're explaining explanation.

另一点是，要表明，尽管贝叶斯推理被称作是独特且最优的方法，但其实我们仍能将它拆解成各个组成部分，分别加以审视，把它们看作是一组在科学家心中，或在普通人试图理解世界时汇聚在一起的价值观。

And the other thing is to show, you know, even though Bayesian reasoning is supposedly this unique and optimal thing to do, can actually kind of tease apart all the pieces, isolate them, and consider them, you know, as a sort of set of values that come together in the mind of a scientist, let's say, or, you know, in the mind of somebody off duty trying to make sense of the world.

所以我认为，扎克和我正是从这里开始的。

So I think that's where Zachary and begin.

然后，问题是，好吧。

Then, you know, the question is, okay.

这些价值观究竟是什么？

What are these values?

在将这个算法拆解成这些小的子模块之后，你可能会说。

Having teased apart this algorithm, you might say to, like, these little sub modules.

好的。

Okay.

这些推理过程的子模块是什么？

What are these sub modules of the reasoning process?

它们是什么样子的？

What do they look like?

我们能给它们命名吗？

Can we name them?

它们是否与我们在科学记录中所见的内容相关，比如科学家是如何解释事物的？

Do they tie into things that we've seen in, let's say, the scientific record itself, meaning how scientists have explained things?

我们能否看到这些单元与科学哲学中的内容相联系，即哲学家们讨论过的决策方式？

Can we see these units tying into things in the philosophy of science, things that philosophers have talked about as ways to make decisions?

我们能否看到它们与超越科学的文化解释实践相联系？

Can we see them tying into cultural practices of explanation that go beyond science?

那么，我们能否看到它们与我们讲故事的方式、记者如何解释事物、历史学家如何解释事物，以及我们如何在日记中向自己解释事物联系起来？

So can we see them tie into, let's say, how we tell stories, how journalists explain things, how historians explain things, how we explain things to ourselves in our diaries, right?

就这样一直下去，对吧？我们的目标其实是想说，从外面看，你似乎拥有一个统一的、最优的推理算法，但当你打开引擎盖往里看时，会发现它其实像一个装满小零件的珠宝盒，这些部件远比我们预期的更具哲学性、更富含价值判断。

So on and on, right, that was kind of our goal there was to sort of say, you know, look, from the outside it looks like you have this unitary optimal algorithm for sense making and yet actually you know what when you open the hood and look inside it's a collects like this little jewel box of pieces that actually look far more philosophical far more value laden than we might expect.

所以，是的，我喜欢这一点的地方在于，再次回到之前关于复杂性思维中多元主义的讨论，当西蒙说你可以用贝叶斯推理方法来思考人们在判断什么是令人满意的解释时所使用的不同启发式方法。

So, yeah, I mean, the thing I love about this is, again, to cast back to earlier notes on pluralism in complexity thinking, that when Simon says you can use a Bayesian inference approach to think about the different heuristics that people are applying to what constitutes a satisfying explanation.

有些人想要一个宏大、全面、融会贯通的模型。

Some people want the big all encompassing, consilient model.

有些人则偏好简约的模型，也就是能迅速写下来的那种，但若将这两种方法中的任何一种推向极端而排斥另一种，就会导致病理性的后果，对吧？

Some people want the parsimonious model that's like, you can write it down really quickly, but either one of those approaches pursued to the exclusion of the other leads to a pathology, you know?

他提到，阴谋论就是这样一个例子：那种能够推动科学理论重大突破的洞察力，如果没有简约性作为制衡，最终会让人陷入像QAnon这样的疯狂之中。

And he talks about that, you know, the conspiracy thinking as being an instance where the kind of brilliance that allows for great revolutions in scientific theory without a parsimonious counter check ends up spinning people off and doing insanities like QAnon.

因此，这个问题在于，如果你从纵向切开来看，再像我们之前说的那样，审视个体能动性与制度需求之间的内在张力。

And so, it's this issue of if you cut that vertically, and then you look at, again, like we were saying, the inherent tensions between individual agency and institutional demand.

我们和迈克尔·拉赫曼聊过很多次，他研究为什么细胞从身体中获取营养物质是有代价的，因为如果这很容易，癌症就会不断肆意增殖，对吧？

We talked about this a lot on the show with Michael Lachman and his work on why it's costly for cells to requisition nutrients from the body, because if it were cheap, then cancer would just proliferate constantly, you know?

所以人类思维中存在一种类似的缺陷，就是：为什么我不能得到我想要的？

And so there's like a flaw kind of in human thinking of like, well, why can't I have what I want?

对吧？

Right?

这并不只是儿童特有的。

And this is not unique to children.

这可不是说，你知道的，我为什么不能一整天都吃糖？

This is not like, you know, why can't I just eat sugar all day?

实际上，现在是个很好的时机，来回顾一下我们即将讨论的上两期节目，然后再稍微谈谈你当前的项目，这些项目与这两个问题都密切相关。

Well, actually, it's it's a good moment to reflect some of the two last episodes that we're gonna discuss before talking a little bit about your current projects, which bear on both of those issues.

一期是关于儿童和儿童发展的，艾莉森；另一期则涉及信息的真正定位问题，正如凯勒最近的著作中所探讨的那样，非常有趣。

One is on children and children's development, Alison, and then this notion of where the locus of information really is, right, in Caleb's recent book and interesting information.

所以，我们先听一听凯勒怎么说。

So let's listen to Caleb first.

实际上，来自圣塔菲研究所的研究员科斯莫斯·沙夫在2010年写过一篇博客文章，提出观点：奇点已经发生，并在1918年底就结束了。

There's a blog entry actually from SFI researcher, Cosmos Scharf, about this back from 2010, where he makes the case, the singularity has already happened, and was over by the close of 1918.

正是工业革命让我们看待像公司这样的事物时，会发现它们的运作方式——正如西蒙·德迪奥上周在圣塔菲研究所的研讨会上所说的，这些机构借鉴了西方秘传传统中的‘聚合体’，就像琳恩·马古利斯所论证的那样，细菌通过内共生的方式结合在一起，形成了复杂的细胞。

It was the industrial revolution that we look at things like corporations, and we see how these things function in what Simon DeDeo in the seminar he gave at SFI last week would call borrowing from Western hermetic traditions, the aggregor, which are these bodies that we participate in the same way that Lynn Marghetis argued bacteria came together endosymbiotically to form complex cells.

为了给人们另一个关联点，你还可以看看杰西卡·弗拉克的研究，特别是她关于粗粒化作为下行因果机制的论文，她认为，即使在相对不那么复杂的生物，比如猕猴中，它们试图在社会中建模和理解彼此的努力，最终会形成这些集体计算，进而塑造行为。

And then you've got, you know, just to give people another point of association for this, you've got Jessica Flack's work, in particular, you know, her paper on coarse graining as a downward causation mechanism, arguing that even in less sophisticated, if you will, organisms like macaques, that their efforts to model and understand one another in society end up leading to these collective computations that then shape behavior.

所以，再次回到马歇尔·麦克卢汉所说的：我们塑造了工具，随后工具也塑造了我们。

So, again, back to this kind of Marshall McLuhan thing about how, you know, we shape our tools and thereafter our tools shape us.

而这就是我想深入探讨的地方

And this is where I'd like to dig in a

再多谈谈你提到的这些观念的负担，以及我们自身的智慧与实际追踪和参与数据日益增长的复杂性之间的张力，

little bit more on what you've

你说过，人类五万年前的脑容量比现在更大，我们实际上已经失去了脑容量，就像我们开始使用叉子后，下颌骨就开始缩小一样。

said about the burden of these ideas ideas and about the tension between our own intelligence and, you know, the ability to actually track and participate in the ratcheting complexity of the data home and the way that it leads to the brain case of human beings fifty thousand years ago was greater than the brain case of humans now, that we've actually lost brain volume in the same way that our jaw started to shrink after we started using forks.

所以，我很想听听你对这一点的进一步阐述。

And so that's something I'd love to hear you refine.

是的。

Yeah.

我之前不知道关于脑容量的这个观察，这非常有趣。

I didn't know about the brain case observation, which is very interesting.

我的意思是，脑容量作为一个衡量标准其实挺特别的，对吧？

I mean, you know, brain size is a peculiar measurement of things, right?

长期以来，人们一直认为脑容量与一个人的聪明程度或复杂程度相关，但事情并没有那么简单。

I mean, for a long time, people assumed brain size correlated with how smart you could be or how sophisticated you could be, but it's not so clear that it's that simple.

因此，即使我们的祖先在五万年前拥有更大的大脑，那他们为什么需要更大的脑容量呢？

And so even something like a larger brain for our ancestors fifty thousand years ago, well, why did they need a larger brain volume?

这可能是对气候条件的一种生理反应。

It could have been a physiological thing in response to climate conditions.

也可能与他们获取食物的方式有关。

It could have been something to do with how they had to operate to get food.

他们可能比今天地球上任何人都更依赖体力劳动。

They may have been much more physical than anyone on the planet today.

我不确定。

I I don't know.

我只是在推测。

I'm just speculating.

你知道，这很有趣。

You know, it's interesting.

你看看大象的大脑。

And you look at brains of elephants.

对吧？

Right?

它们大脑的不同区域比例也不同。

They have differently proportioned regions of their brains.

其中一部分无疑是因为它们体型庞大，需要更多的神经元来应对。

And some of that is undoubtedly because they have a large body and they need neurons to deal with that.

运动这一行为所涉及的计算量，可能远超我们人类的运动所需，尽管我们本身已经非常复杂。

And the act of movement has to engage perhaps a lot more computation than act of movement, even for something like us, although we're pretty complex.

所以，这确实非常非常有趣。

So yeah, so it's very, very interesting.

我认为，这确实与你所说的那样紧密相连——即我们作为物种在世界上取得的成功，与我们作为个体、我们的基因谱系以及整个物种持续繁衍的概率之间的张力，以及我们周围的一切。

And I think, yeah, it does connect through to, as you put it so nicely, this tension between sort of our success in the world as a species or just the probability of us continuing to propagate both as individuals and our particular gene lineages and our species, gene lineage and so on and everything around us.

而数据在诸多方面帮助了这一点，或看似在帮助，却也带来了非凡的负担。

And how the data helps with that or seems to help with that in so many ways yet does present this extraordinary burden.

我认为这种负担变得更加明显了，但它一直以来都以某种方式存在着。

And I think that burden has become much more evident, but it's always been there to some extent.

你提到人们谈论20世纪初发生的奇点，我觉得这很有趣。

You mentioned people referring to how singularity happened in the early 1900s, which I think is lovely.

我认为那可能是一个更好的参照点。

And I think that may well be a better sort of point of reference.

但二十世纪初发生的一些事情，关于信息——以我们今天对信息的理解——几乎是数字信息，非常有意思。

But, you know, rather interesting things that were going on in the early nineteen hundreds to do with information as we think about it today, almost digital information.

比如打孔卡片，我在书中提到过，几十年来，打孔卡片一直是工业、金融等领域存储信息的主要方式。

So punched cards, something I talk about in the book, and punched cards were for many decades, the primary way of storing information for industry, for finance, all those things.

你有打孔卡片机、打孔卡片阅读器。

You got punch card machines, punch card readers.

第一代数字计算机、第一代商用数字计算机都使用打孔卡片进行编程、数据输出和存储等。

The first digital computers, first commercial digital computers utilize punch cards for programming, for data output and storage and so on.

有趣的是，这些是实实在在的物理物品。

And what's so interesting is those were tangible physical things.

它们不是那些我们永远看不到的隐形硅片，除非你把芯片刮开。

They weren't invisible pieces of dope silicon that none of us ever get to look at unless you scrape away your chip.

它们在现实中非常具体，却给我们的资源带来了巨大的负担。

They were very tangible in the world and they represented a very significant burden on our resources.

我认为人们已经忘记了这一点。

And I think people have forgotten that.

但如果你深入研究这段历史，会发现它非常引人入胜。

But if you dig into the history of this, it's really fascinating.

你知道吗，仅在美国，就在20世纪60年代中期的高峰期，每年生产的穿孔卡片至少有2000亿张。

You know, just the production of punch cards at the peak just in The US in, I think, the mid 1960s, there were at least something like 200,000,000,000 punch cards being manufactured every year.

而且，这些卡片本身是相当大的纸片，而打孔这个物理动作也需要消耗能量。

And, you know, they're a sizable piece of card or paper, and then you have the physical act of punching them takes energy.

你还需要把这些卡片裁剪成型。

You have to cut those things around.

我不知道这加起来有多少吨，但我肯定数量相当可观。

I don't know what tonnage that amounted to, but I'm sure it was significant.

人们只是不断生产越来越多这样的东西。

And people were just producing more and more of these things.

关于打孔卡，有趣的是它让我们非常清楚地看到人类所承受的负担。

And what's so interesting about punch cards is they make it very easy to see the burden on humans.

因此，制造大量纸张、生产这些卡片、打印、打孔，都构成了负担。

So there's a burden of making all that paper, producing all these things, printing them, punching them, so on.

但人类还得把这些东西搬来搬去。

But then humans had to carry the things around.

如果你是上世纪50年代、60年代，甚至70年代的一名科学家，想在计算机上运行一段程序，通常得把程序打在打孔卡上，然后亲自带着它去机器前，逐张插入，再取回，最后小心翼翼地放进文件柜里保存，如此反复。

If you were a scientist and you wanted to run a piece of code on a computer back in the 50s, 60s, even into the 70s, very often you would have to put your program on to punch cards and then carry it physically and stand there and feed it into the machine and then retrieve it and carry it physically and put it somewhere safe in your filing cabinet and so on and so on.

你是在消耗自己的体力。

You were expending your energy.

你知道吗？你吃掉的汉堡包，最终成了你后来进行信息处理的能量来源。

You know, the hamburger you had eaten ended up fueling your act of information processing later on.

当然，打孔卡最终被抛到了技术发展的路边，因为它们不够灵活。

And, of course, punch cards went onto the side ditches of the roads of technology because they weren't terribly flexible.

它们在信息的数字化存储、检索和使用方面，效率不如纯电子方式。

They weren't as efficient as purely electrical digital information storage and retrieval and utilization.

但今天，我们生产的數據量却出现了荒谬的增长。

But today, we have this ridiculous growth in the amount of data that we produce.

我们的物种每天、每24小时就产生大约25亿亿比特的新数据。

It's something like 2.5 quintillion bits of new data are generated by our species every single day, every 24 hours.

这相当于每24小时产生的数据量是莎士比亚全部作品的万亿倍。

And that's something like a trillion times all of Shakespeare's products every 24 hours.

而其中大部分或相当一部分数据都被某种程度永久地存储了下来。

And most of that or a lot of it is finding itself somewhat permanently stored.

而且这些数据包罗万象。

And it's everything.

对吧？

Right?

这就是将数字比特记录下来的过程。

It's this conversation, being to recording digital bits.

这是你拍摄的视频。

It's the video you made.

这是你用手机拍的照片。

It's the picture you took on your phone.

这是所有的金融交易。

It's all the financial transactions.

这是所有的科学计算。

It's all the scientific computation.

这是支撑互联网等所有事物的内容。

It's everything in supporting the Internet and so on.

而这一切当然都需要能量。

And that, of course, all takes energy.

首先需要建造这些技术，这本身就需要大量能源。

It takes the construction of the technology in the first instance, which is very energy intensive.

制造硅芯片是一项极其耗能的事情，因为你需要从高度无序的材料中创造出极其有序的结构。

Making silicon chips is an extraordinarily energy intensive thing because you're making these exquisitely ordered structures out of very disordered material.

因此，我们又回到了热力学，从某种局部意义上说，是在与熵作斗争。

And then so there too, we go back to thermodynamics and fighting in the sense against entropy in some local fashion.

这需要大量的能量。

That takes a lot of energy.

我们需要发电来为当前这个数字信息世界提供动力，也就是数据的这一部分。

We're we're having to generate electricity to power current digital informational world, that piece of the data.

令人深思的是，我们目前投入于此的能源和资源总量，已经相当于大约7亿人总的新陈代谢输出或消耗量。

The rather sobering thing is that already the amount of energy and resources that we're putting into this it's about the same as the total metabolic output or utilization of around 700,000,000 humans.

如果你观察计算、数据存储和数据传输的能源需求趋势，所有趋势都在上升。

And if you look at the trends in energy requirements for computation, for data storage data transmission, the trends are all upwards.

这是一条指数曲线。

It's an exponential curve.

它们表明，即使我们能在效率上有所提升，除非这些提升极其显著，否则在几十年后，我们运行数字数据所需的电能总量，可能会与当今全球文明所消耗的总电能相当。

And they suggest that perhaps even if we have some improvements in efficiency, unless those improvements are extraordinary, then in a few decades' time, we may be at a point where the amount of energy, just electrical energy, required to run our digital data home is roughly the same as the total amount of electrical energy we utilize as a global civilization at this time.

这适用于所有方面。

That's for everything.

这包括点亮你的灯、运行水厂的水泵、给你的电动汽车充电等等。

That's for putting on your lights, running the pumps in your water plants, charging your electric vehicle these days and so on and so on.

我们的信息世界也将达到同样的规模。

That will be matched by just our informational world.

所以当你看到这一点时，你会觉得这可能是个问题。

So you look at that and you think this might be a problem.

所以，是的，我认为凯莱布的东西真的很洛夫克拉夫特风格，对吧？

So yeah, Caleb's stuff is really kind of Lovecraftian, I think, right?

你知道，因为他在他的对话中精确地量化了，通过我们的互动所形成的制度和系统如何对我们的行为施加压力，并对生物圈的新陈代谢过程提出越来越高的要求，对吧？

You know, because he's really figured in his conversation about the data home precisely how in a quantifiable way, the pressure of the institutions and systems that have emerged through our interactions are placing a burden on our behavior and an increasing demand on the metabolic processes of the biosphere, right?

这就像莫洛克，对吧？

Like this is Moloch, right?

人们谈论这个，你知道， Slate Star Codex 的表述中提到，有一种东西从我们之中浮现出来。

And people talk about this, you know, this slate star codex articulation that there is this thing that emerges out of us.

这可以说是Egregore的恶魔化身，对吧？

It's sort of the demonic figuration of the Egregore, right?

这是集体潜意识的阴暗面。

It's the dark side of the collective consciousness.

是的。

Yeah.

我特别喜欢和凯莱布的那场对话，因为某种程度上，这很美——我们的文化传承每天、每时每刻都在变得更加丰富和深厚。

And the thing that I loved about that conversation with Caleb was that in a way it's beautiful, like our cultural inheritance grows richer and deeper by the day, by the moment.

对，那我们干脆就以这个观点作为结尾吧，我觉得这非常有趣。

Yeah, so let's just end now with I think this is very interesting.

我的意思是，这对你来说很个人化，因为你最近有了孩子，虽然也不是特别近了，但你确实有孩子了。

I mean, it's personal for you because you recently had well, not that recently, but you have children.

而且对我而言，也有其他方面的个人意义，因为我对噪音很感兴趣，最近我和大卫·威尔珀特与丹尼·卡内曼等人有过一场辩论，我们认为噪音对于复杂世界是绝对必要的，而他们和许多人则认为它无关紧要。

And and in fact, personal for me in other ways because of my interest in noise and a recent debate that David Wilpert and I had with Danny Kanemann and and others where we believe noise is absolutely essential to the complex world, and they and many others believe it's inessential.

艾莉森有一些非常出色的研究，实际上表明，童年最关键的特点或许是噪音的极端重要性。

And Alison has some very nice work showing actually that perhaps the key characteristic of childhood is the incredible importance of noise.

那我们就再听一遍吧。

So let's just listen to that a little.

让我们通过你的论文《童年作为探索与利用张力的解决方案》来分析一下。

Let's lens this through your piece Childhood as a Solution to Explore Exploit Tensions.

我喜欢优秀的综述论文。

I love a good review paper.

我喜欢那种能把所有内容融会贯通的论文，这篇就是其中之一。

I love a paper that just brings it all together, and this is one of those.

你能帮助人们理解人类有多么奇特吗？

And can you help people understand how weird we are as human beings?

正如我所说，我最初提出这个问题，是想了解我们可以从孩子身上学到什么，关于学习是如何可能的。

As I say, I started out asking this question about what we could learn from children about how learning is possible.

但还有一个更深层的问题，那就是为什么孩子尤其似乎拥有如此惊人的学习能力？

But there's another kind of meta question, which is why is it that children especially seem to have these incredible learning capacities?

这与一个更广泛的问题相关，那就是：为什么人类会有童年阶段？

And that's connected to a broader question, which is why do children exist at all?

为什么我们人类会有这么长的不成熟期？

Why do we, as humans, have this long period of immaturity?

当我开始研究这背后的进化生物学背景时，我发现这一点尤为突出，因为我们的童年期长度是我们最接近的灵长类近亲的两倍。

And the more I started looking at the sort of evolutionary biology background for this, the more striking it is because we actually have a childhood that's twice as long as that of our closest primate relatives.

黑猩猩到了七岁时，产生的食物就已经和它们消耗的一样多了。

Chimpanzees, by the time they're seven, are producing as much food as they're consuming.

即使在狩猎采集文化中，人类也要到至少十五岁，甚至更晚才达到这一点。

And even in forager cultures, humans aren't doing that until at least age 15, if not later.

这确实令人困惑。

So that's really puzzling.

为什么我们会有如此漫长的童年期？

Why do we have this very long period of childhood?

结果发现，这不仅适用于人类。

And then it turns out that, in fact, this isn't just true about humans.

动物的童年期长短与其神经元数量、大脑大小，通俗地说，与其智力水平，以及对学习世界依赖程度之间，存在一种普遍的关联。

There's a very general relationship between how long a period of childhood an animal has and how many neurons it has, how big a brain it has, anthropomorphically how smart it is, certainly how much it relies on learning about the world.

在进化生物学中，人们提出过一种观点，认为正是这段长期受保护的时期，才使我们能够学到如此多的知识。

And in evolutionary biology, people have talked about the idea that it is that long protected period that actually enables you to learn as much as you do.

因此，童年时期有着某种特别之处，它使得人类在儿童时期的不成熟程度上远远超出其他物种，也使得整个群体、整个物种必须投入巨大资源来确保这些孩子的生存。

So there's something really special about childhood and it makes humans in particular go way out on the end of the distribution in terms of how immature we are as children and how much investment as a group, as a species, we have to put into just keeping those children alive.

所以最初一个模糊的普遍想法是，拥有更长的学习时间可能是童年的优势。

So the sort of vague general idea to start out with was, well, just having more time to learn might be the advantage of childhood.

但当你特别关注神经科学时，就会发现这不仅仅是儿童存在的时间更长。

But when you look at especially at neuroscience, that it isn't just that children are around for longer.

他们的大脑和学习方式在根本上与成人有着截然不同的形式。

They really have foundationally different kinds of forms of brain and forms of learning compared to adults.

而其中许多特征看起来像是缺陷，比如难以集中注意力，难以进行长期规划。

And many of these are actually things that might look like bugs, like not being very good at having focused attention, not being very good at long term planning.

我们为什么要这样呢？

Why would we do that?

为什么我们的生命中会存在这么一段看似如此无能为力的漫长时期？

Why would we have this long period in our lives where we seem to be so incapacitated?

那这为什么会与我们的学习能力相关呢？

And why would that be connected to our capacities for learning?

当我开始从事人工智能研究时，计算机科学中反复出现的一个非常普遍的想法就是探索与利用的权衡。

So when I started doing the work in AI, one of the really very general ideas that comes across again and again in computer science is this idea of the explore exploit trade off.

这个想法是，你无法让一个系统同时兼顾有效执行任务的能力。

And the idea is that you can't get a system that simultaneously going to optimize for actually being able to do things effectively.

这就是利用的部分。

That's the exploit part.

以及探索所有可能性的能力。

And being able to figure out, search through all the possibilities.

假设你有一个想要解决的问题，或者一个想要发现的假设。

So imagine that you have some problem that you want to solve or some hypothesis that you want to discover.

你可以把它想象成一个装满所有可能假设、所有可能解决方案、所有可能策略的大盒子。

And you can think about it as if there's a big box full of all the possible hypotheses, all the possible solutions to your problem, all of the possible policies that you could have.

例如，你处于一个强化学习的环境中。

For instance, you're in a reinforcement learning context.

现在你处于这个盒子中的某个特定位置。

And now you're at a particular space in that box.

这就是你目前所知道的。

That's what you know now.

这就是你目前拥有的假设。

That's the hypotheses you have now.

这就是你目前拥有的策略。

That's the policies you have now.

现在你想要做的是到达另一个地方。

Now what you want to do is get somewhere else.

你希望找到一个新想法、一个新的解决方案。

You want to be able to find a new idea, a new solution.

你该怎么做呢？

How do you do that?

实际上，你可以采用两种不同的策略。

And there are actually two different kinds of strategies you could use.

其中一种方法是，你可以寻找与你已有方案非常相似的解决方案。

One of them is you could just search for solutions that are very similar to the ones you already have.

你可以在你现有的想法基础上做些微小调整，以适应新的证据或新问题。

And you could just make small changes in what you already think to accommodate new evidence or a new problem.

这种方法的优势在于，你能很快找到一个相当不错的解决方案。

And that has the advantage that you're going to be able to find a pretty good solution pretty quickly.

但它也有一个缺点。

But it has a disadvantage.

缺点是，在这个高维空间中，可能存在一个远优于你当前方案的更好解。

And the disadvantage is that there might be a much better solution that's much further away in that high dimensional space.

任何有趣的领域都大到无法完全系统性地搜索。

And any interesting space is gonna too large to just search completely systematically.

你总是需要选择考虑哪些可能性。

You're always gonna have to choose which kinds of possibilities you wanna consider.

因此，可能存在一个非常优秀的解决方案，但它与你当前所处的位置差异极大。

So it could be that there's a really good solution, but it's much more different than where you currently are.

问题是，如果你只是做类似所谓的爬山法那样的事情，只在局部范围内寻找，你很可能会陷入所谓的局部最优解。

And the trouble is that if you just do something like what's called hill climbing, you just look locally, you're likely to get stuck in what's called a local optimum.

因此，你很可能进入一种状态：任何微小的改变都会让情况变得更糟。

So you're likely to get into a position where every small change you can make is gonna make things worse.

所以，这会让你觉得你最好就待在原地不动。

So it's gonna look like you're just should stay stay where you are.

但这些大的改变本可能让情况变得更好。

But these big changes could have made things better.

通常，各种方法解决这个问题的方式是：一开始进行广泛而大量的搜索，涵盖众多可能性。

And the way that typically gets resolved in various kinds of forms is start out with this big broad search with lots and lots of possibilities.

在各种可能性之间跳跃，然后逐渐降温并缩小范围。

Jump around from one possibility to another and then slowly cool off but narrow down.

常用的比喻是关于温度的。

And the metaphor that's often used is about temperature.

所以，你可以想象，如果这些大盒子里面装的是空气分子，而不是假设。

So you can think about big boxes if it had air molecules in it instead of hypotheses.

低温搜索就是一种几乎不怎么移动的搜索。

A low temperature search would be just a search where you weren't moving very much.

高温搜索则是一种更大、更嘈杂、更随机、更跳跃的搜索。

The high temperature search would be this big, much noisier, more random, bouncy kind of search.

我有时候会问，家里有四岁孩子的人，这两种搜索哪种更像你的四岁孩子？

And I like to say sometimes for anyone who has a four year old at home, which of those sounds more like your four year old?

四岁孩子在字面和隐喻意义上都是嘈杂且活泼的。

Four year olds are both literally and metaphorically noisy and bouncy.

所以解决方案是先从这种广泛而宽泛的搜索开始。

So the solution is start with this big broad search.

当然，缺点是你可能会花时间尝试一些非常奇怪、古怪、其实对你帮助不大的东西。

The disadvantage, of course, is that you might be spending time trying out really weird, strange things that aren't gonna help you very much.

当你发现某个方向看起来大致正确时，就缩小范围，转向更冷静的解决方案。

And then when you see something that looks like it's in the right ballpark, narrow in to the cooler solution.

这就像是冶金中的退火过程：先将金属加热，然后逐渐冷却，最终得到更坚固的金属。

So it's like what happens in metallurgy with annealing where you heat up a metal first and then gradually cool it to end up with a more robust metal.

但当然，如果你从这个角度——即探索与利用的对比，或者高温、低温退火的角度——来看待童年，那么许多看似是缺陷的东西，实际上却是优点。

But of course, if you're thinking about childhood from that perspective, from the perspective of that kind of explore exploit contrast or from the perspective of the high temperature, low temperature annealing, then a lot of the things that look like bugs turn out to actually be features.

因此，进行大量的随机变化、保持嘈杂、拥有宽泛的注意力潜力，而不是狭窄的注意力潜力。

So actually doing a lot of random variability, being noisy, having a broad focus potential instead of a narrow focus potential.

所有这些在利用视角下其实并不好，因为当你只想快速有效地实施某项策略时，这些特质反而成了障碍。

All those things that are really not good from the exploit perspective when what you want to do is just implement that policy, say, as quickly and effectively as you can.

但从探索的角度来看，这些特质却都成了真正的优势。

Those things all turn out to be real benefits from the explore perspective.

你真正想要的是尽可能多地了解这个世界，探索尽可能多的可能性。

What you want is to learn as much as you can about the world and explore as many possibilities as you can.

这非常美妙，因为我觉得这一洞见回应了美国社会中特别普遍的一些问题，比如艺术资助的最终实用价值，或者当默里·盖尔曼谈到为何资助像圣塔菲研究所这样的机构如此困难时——基础理论不像疫苗研发那样有明确的即时用途。

This is really beautiful because I think this insight speaks to questions that seem especially pervasive in American society about the ultimate practical utility of arts funding, for instance, or when Murray Gell-Mann talked about why it is so difficult to fund an institution like SFI, where fundamental theory, unlike the search for a vaccine is something that doesn't have an immediate pre specified use.

就像你不知道最终会得到什么。

Like you don't know what you're gonna get out of it.

你可能几十年后才明白。

You may not know for decades.

这是一种高风险的追求，但这也正是游戏存在的原因，你知道的，艾莉森的研究与此密切相关。

It's a high risk pursuit, but that's why play exists, you know, and Alison's work is linked very intimately.

我和她在与安德烈亚斯·瓦格纳的那期对话中讨论过这个话题，还有他关于游戏在进化中的作用的研究。

And I spoke about it with her in that episode with Andreas Wagner and, you know, the work that he's done on the evolutionary utility of play.

所以，这里我们又看到一个例子：对儿童、或任何类型的幼年生物、或新组织而言，年轻且具有探索性是一种优势，就像我儿子喜欢爬到椅子上，他简直着迷于爬到桌子顶上，你知道吗？

And so again, here we have an instance where the benefit to a child or, you know, a juvenile organism of whatever kind or a new organization to be young and exploratory and to, like my son climbs up on chairs, like he's obsessed with getting on top of the table, you know?

而他根本不在乎。

And he just doesn't, he doesn't care.

他根本无法理解，如果我们告诉他，如果他从上面摔下来撞破头会有什么后果，他是不可能懂的，对吧？

Like he doesn't, you know, it's impossible for us to communicate to him what he stands to lose if he falls off and cracks his head, right?

幸运的是，进化不断重复地发现了一种平衡：那些年长、饱经风霜的人懂得其中的风险，但他们也因此变得规避风险、思维保守，而他们的孩子却不是这样。

And so there is thankfully this thing that evolution keeps finding again and again and again, which is a balance between those who are older and battle scarred and they get it, but they've become risk averse and conservative in their thinking in a way that their children aren't.

这两者彼此制衡。

And the two of them balance each other.

这太完美了，因为现在让我们设想，我们刚刚孕育出了迈克尔·加菲尔德。

So this is perfect because let's now view us as having given birth to Michael Garfield.

因此，对于一个几乎不惧风险、具有高度探索性的人来说，让我们在回顾了你制作的这些精彩剧集之后，转向一个稍微不同的思考角度。

And so in terms of someone who's hardly risk averse, who's high noise, in that sense, exploration, let's pivot now, having reviewed these amazing episodes that you made, to a little bit of a consideration.

让我先就这一点发表一些看法，然后再提出一些问题。

Let me just make some remarks on this and then ask some questions.

挺有意思的，我相信许多听众，当然还有你身边了解你的人，都知道你拥有这样一种极其有趣的、高温般的思维，总是不断寻找各种联系。

Sort of interesting, I'm sure many people listening and certainly your friends here who know you are aware of this extremely interesting high temperature mind that is constantly looking for connections.

有些联系是真实的，从我的角度来看，有些则未必，但这种探索是必要的，因为如果没有这种探索，艾莉森所提出的关于儿童发展的模拟退火模型，你就永远不会涉足那些领域。

Some are real, some are not so real from my point of view, but it but it's necessary because without that Alison's, you know, simulated annealing model of childhood development, there are just areas that you never would have moved into.

那些领域将会被忽视，而我们会变得僵化。

They would have been neglected, and we would become sclerotic.

所以我想谈谈下一个阶段，你对即将撰写的书籍，以及我知道你计划制作的更多播客的这种高温探索。

So I want to talk a little bit about this next phase, your sort of high temperature exploration of both the book that you're planning, but I also know more podcasts that you want to do.

所以，请跟我们说说你接下来的发展方向。

So tell us a little bit about the next phase in your development.

是的。

Yeah.

有一位芝加哥大学的媒体理论家，W.J.T. 米切尔，他写了一部精彩的作品，作为瓦尔特·本雅明《机械复制时代的艺术作品》的后续。

So there's a a University of Chicago media theorist, WJT Mitchell, who wrote a fantastic follow-up to Walter Benjamin's essay, the Our Art in the Age of Mechanical Reproduction.

米切尔写了一篇题为《贝叶斯控制论复制时代的艺术》的文章，其中提出：我们需要一种对当下的古生物学，从深时的视角重新思考我们的处境，以形成足以应对我们所面临挑战的艺术与科学的综合。

Mitchell wrote an essay called Art in the Age of Beyer's Cybernetic Reproduction in which he said, We need a paleontology of the present, a rethinking of our condition in the perspective of deep time in order to produce a synthesis of the arts and sciences adequate to the challenges we face.

正如我们在这集中已经深入讨论过的那样，出于同样的原因，我认为复杂性科学是一种工具，即使在其自身方法论的多样性中也是如此。

And, you know, for the same reasons we've discussed now at length in this episode, it strikes me that complexity science is one tool, even in all of the plurality of its own methodologies.

从远处看，它仍是一组需要与其他各种方法保持某种生态平衡的技术。

At a distance, it is still one group of techniques that needs to be held in a kind of ecological balance with all of these other techniques.

哦，让我稍微澄清一下。

Oh, let me let me just qualify that a little.

我的意思是，它确实是有界限的。

I mean, there's no doubt that it's circumscribed.

但我对将分析领域与分析技术混为一谈感到有些不适。

But I I would I I bristle a little bit about confounding the domain of analysis with the techniques of analysis.

我一直认为，物理学并不仅仅是微积分。

And I've always thought, you know, physics is not just about the calculus.

对吧？

Right?

是的。

Yeah.

我认为物理学是关于宇宙中由对称性主导的那部分。

And I think physics is about the portion of the universe dominated by symmetry.

复杂性科学是关于宇宙中由对称性破缺主导的那部分。

Complexity science is about the portion of the universe dominated by broken symmetry.

我们所使用的方法会不断变化。

And the methods that we use will constantly change.

对吧？

Right?

我并不是说这同样适用于艺术和音乐，因为我明白你的观点，但我觉得我们不应该把深层次的研究领域与其方法混为一谈。

And I now that's not to say that it it's also the arts and it's music because it's So I take your point, but I do think we shouldn't confound deep areas of inquiry with their methods.

完全合理。

Totally fair.

但我认为我想要表达的是，既有定量方法，也有定性方法，我们需要两者兼备。

But I guess all I'm saying is that there are quantitative approaches and there are qualitative approaches and we need them both.

我们现在面临一个非常现实的担忧：像ChatGPT这样的系统可能直接获得访问权限，或激励并帮助那些拥有遗传学实验室和生物打印机的人创造出能够重塑生物圈的合成生物。

We're at a point now where there's a very real concern that people have that systems like ChatGPT could either gain access to directly or inspire and enable people with access to genetics laboratories and bioprinters to create synthetic organisms that reshape the biosphere.

看看莎拉·沃克与大卫·格林斯普oon等人的研究，可以说，人类世其实并非人类的时代。

You you look at Sarah Walker's work with David Grinspoon, etcetera, and it's arguable that the Anthropocene is really not the age of human beings.

而是我们所创造的这些技术怪物的时代。

It's the age of these technological monsters that we've created.

你知道，像乔治·丘奇这样的研究，灭绝物种的复活在2023年已经提上了日程。

You know, with George Church, like de extinction is on the menu now in 2023.

而且，我们上一次对话中讨论过，你更倾向于认为新冠是通过穿山甲或蝙蝠等动物跨种传播的，还是认为它是一次功能获得实验意外泄露的结果。

And, you know, in our last conversation, we talked about how, like, whether you prefer to ascribe to the notion that COVID-nineteen was a zoonotic transmission that crossed over from a pangolin or a bat or whatever, or that it was a gain of function experiment that got loose.

我们再次讨论的是，当我们把一切拼凑在一起时，却把那些本应分开处理的事情混为一谈了。

What we're talking about again is the way that we, you know, in patching everything together, we have folded over these things that might have best been kept separate.

你知道，这很有趣，因为你提出了一个非常有意思的观察。

You know, it's interesting because it raises you make a very interesting observation there.

最讽刺的是，彼得·蒂尔把他的监控公司命名为帕兰提尔。

The most ironic version of this is Peter Thiel calling his surveillance company Palantir.

我的意思是，他真的读过《指环王》吗？

I mean, did he actually read Lord of the Rings?

如果你还记得帕兰提尔水晶是如何掌握和控制一切，以及它们有多危险的话。

And if you remember what the Palantir who had mastery and control and how dangerous they were.

我觉得特别有趣的是，似乎权力与控制的美学，压倒了负责任的伦理。

And I think it's very intriguing to me that there seems to be this somehow, the aesthetics of power and control beat the responsible ethics.

尽管克莱顿既是伟大的道德家，也是科技及其他诸多领域的预言家，他观察到的许多事情都已成真，但伦理成分却只是人们从权力博弈中获取营养时撒的一点盐和胡椒。

Somehow, even though Crichton was a great moralist as well as a prognosticator on technology and and so many things he observed seem to have come true, but somehow the ethical component is just the sort of salt and pepper on the nutrition that people derive from the power play.

这才是真正持久的东西。

That's the thing that persists.

对吧？

Right?

但最终，不幸的是，它留下的残余并不是更高的道德责任，

That's in the end, unfortunately, what it leaves behind in its residue is not greater moral responsibility,

但

but

更强的对全面控制的渴望和需求。

a greater appetite and desire for total control.

一个很好的例子是GPU从美学到实用再到伦理的演变轨迹。

A good example of that interesting aesthetic to practical to ethical trajectory is GPUs.

对吧？

Right?

我的意思是，GPU在晶体管时代原本是游戏玩家的玩意。

I mean, GPUs were gamer fare in the transistor world.

然后它们逐渐转向了区块链和加密货币。

They then migrate to, you know, blockchain and crypto.

而现在它们支撑着大型语言模型。

And now they're supporting large language models.

对吧？

Right?